Real-time Gait Monitoring System for Consumer Stroke Prediction Service

Gait monitoring is considered as a significant marker of disability, injury, and gait symmetry. The goal of this study is to develop a real-time consumer health monitoring system based on IoT sensors and Machine learning technique in order to detect health abnormalities such as, stroke onset. The proposed consumer stroke prediction system consists of IoT based gait monitoring sensors, real-time vital sign monitoring and machine learning based disease prediction model to predict the disordered gait and the healthy gait. This study will be useful for post-stroke gait coordination for rehabilitation and consumer health monitoring service.